Department of Biomedical Engineering, Yale University School of Medicine USA.
Department of Biomedical Engineering, Yale University School of Medicine USA; Radiology and Biomedical Imaging, Yale University School of Medicine USA; Interdepartmental Neuroscience Program, Yale University School of Medicine USA.
Neuroimage. 2022 Feb 15;247:118792. doi: 10.1016/j.neuroimage.2021.118792. Epub 2021 Dec 8.
Mapping the human connectome and understanding its relationship to brain function holds tremendous clinical potential. The connectome has two fundamental components: the nodes and the sconnections between them. While much attention has been given to deriving atlases and measuring the connections between nodes, there have been no studies examining the networks within nodes. Here we demonstrate that each node contains significant connectivity information, that varies systematically across task-induced states and subjects, such that measures based on these variations can be used to classify tasks and identify subjects. The results are not specific for any particular atlas but hold across different atlas resolutions. To date, studies examining changes in connectivity have focused on edge changes and assumed there is no useful information within nodes. Our findings illustrate that for typical atlases, within-node changes can be significant and may account for a substantial fraction of the variance currently attributed to edge changes .
绘制人类连接组图谱并理解其与大脑功能的关系具有巨大的临床潜力。连接组有两个基本组成部分：节点和它们之间的突触连接。虽然人们已经关注于生成图谱并测量节点之间的连接，但还没有研究检查节点内的网络。在这里，我们证明每个节点都包含重要的连接信息，这些信息在任务诱导状态和个体之间系统地变化，因此基于这些变化的测量可以用于分类任务和识别个体。结果不仅适用于任何特定的图谱，而且适用于不同的图谱分辨率。迄今为止，研究连接变化的研究都集中在边缘变化上，并假设节点内没有有用的信息。我们的研究结果表明，对于典型的图谱，节点内的变化可能非常显著，并且可能占当前归因于边缘变化的方差的很大一部分。
